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Related Experiment Video

Updated: Feb 28, 2026

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Context-Aware Semantic Localization with Adaptive Sensor Fusion Under Adverse Conditions.

Jun-Hyeon Choi¹, Dong-Su Seo¹, Ye-Chan An¹

¹Department of Electrical and Computer Engineering, College of Information and Communication Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea.

Sensors (Basel, Switzerland)

|February 27, 2026

Summary

This study introduces semantic localization for autonomous vehicles, improving accuracy and reliability. By integrating semantic reasoning, it significantly reduces localization errors, especially during sensor issues.

Keywords:

context-aware ontology semantic localization semantic map sensor fusion

Related Experiment Videos

Last Updated: Feb 28, 2026

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Area of Science:

Robotics
Artificial Intelligence
Computer Vision

Background:

Autonomous driving requires robust vehicle localization under challenging real-world conditions.
Conventional methods struggle with sensor degradation and ambiguous data, leading to inaccurate pose estimates.

Purpose of the Study:

To develop a semantic localization framework integrating ontology-based reasoning for enhanced accuracy and reliability.
To address limitations of geometric-based localization in degraded sensor conditions.

Main Methods:

Reformulated localization as a context-aware constraint selection problem.
Integrated semantic reasoning to evaluate logical and contextual validity of pose hypotheses.
Employed ontology-based semantic consistency checks for objects, places, and vehicle poses.

Main Results:

Achieved an average reduction of 35.6% in mean localization error and 47.0% in maximum localization error.
Demonstrated improved robustness and accuracy, particularly under sensor degradation and dynamic environments.
Enabled structured multi-sensor fusion by selecting semantically relevant information.

Conclusions:

Semantic localization significantly enhances autonomous vehicle positioning accuracy and reliability.
The framework reduces computational complexity and is adaptable to cooperative perception systems.
Ontology-based semantic reasoning is crucial for next-generation autonomous driving localization.